This invention relates to the fields of biomarker discovery and personalized medicine, and more particularly relates to a method for predicting, in advance of treatment, whether a non-small-cell lung cancer (NSCLC) patient is likely to obtain more benefit from an Epidermal Growth Factor Receptor Inhibitor (EGFR-I) such as erlotinib or gefitinib as compared to chemotherapy.
Non-Small-Cell Lung Cancer is a leading cause of death from cancer in both men and women in the United States. There are at least four (4) distinct types of NSCLC, including adenocarcinoma, squamous cell, large cell, and bronchoaldeolar carcinoma. Squamous cell (epidermoid) carcinoma of the lung is a microscopic type of cancer most frequently related to smoking Adenocarcinoma of the lung accounts for over 50% of all lung cancer cases in the U.S. This cancer is more common in women and is still the most frequent type seen in non-smokers. Large cell carcinoma, especially those with neuroendocrine features, is commonly associated with spread of tumors to the brain. When NSCLC enters the blood stream, it can spread to distant sites such as the liver, bones, brain, and other places in the lung.
Treatment of NSCLC has been relatively poor over the years. Chemotherapy, the mainstay treatment of advanced cancers, is only marginally effective, with the exception of localized cancers. While surgery is the most potentially curative therapeutic option for NSCLC, it is not always possible depending on the stage of the cancer.
Recent approaches for developing anti-cancer drugs to treat the NSCLC patient focus on reducing or eliminating the ability for cancer cells to grow and divide. These anti-cancer drugs are used to disrupt the signals to the cells to tell them whether to grow or die. Normally, cell growth is tightly controlled by the signals that the cells receive. In cancer, however, this signaling goes wrong and the cells continue to grow and divide in an uncontrollable fashion, thereby forming a tumor. One of these signaling pathways begins when a chemical in the body, called epidermal growth factor, binds to a receptor that is find on the surface of many cells in the body. The receptor, known as the epidermal growth factor receptor (EGFR) sends signals to the cells, through the activation of an enzyme called tyrosine kinase (TK) that is found within the cells. The signals are used to notify cells to grow and divide.
Two EGFR-I anti-cancer drugs that were developed and prescribed to the NSCLC patients are called gefitinib (trade name “Iressa”) and erlotinib (trade name “Tarceva”). These anti-cancer drugs target the EGFR pathway and have shown promise in being effective toward treating NSCLC cancer. Iressa inhibits the enzyme tyrosine kinase that is present in lung cancer cells, as well as other cancers in normal tissues, and that appears to be important to the growth of cancer cells. Iressa has been used as a single agent of the treatment of NSCLC that has progressed after, or failed to respond to, two other types of chemotherapies. There are other drugs in development and in validation that address the same EGFR pathway using different compounds, e.g. the irreversible EGFR-TKI inhibitors affatinib (Boehringer-Ingelheim) and dacomitinib (Pfizer).
The assignee of the present inventors has developed a test known as VeriStrat® which predicts whether NSCLC patients are likely or not likely to benefit from treatment of EGFR pathway targeting drugs, including gefitinib and erlotinib. The test, also referred to herein as “VS 1.0,” is described in U.S. Pat. No. 7,736,905, the content of which is incorporated by reference herein. The test is also described in Taguchi F. et al., J. Nat. Cancer Institute, 2007 v. 99 (11), 838-846, the content of which is also incorporated by reference herein. Additional applications of the test are described in other patents of the present assignee, including U.S. Pat. Nos. 7,858,380; 7,858,389 and 7,867,774, the contents of which are incorporated by reference herein.
In brief, the VeriStrat test is based on serum and/or plasma samples of cancer patients. Through a combination of MALDI-TOF mass spectrometry and data analysis algorithms implemented in a computer, it compares a set of eight integrated peak intensities at predefined m/z ranges with those from a training cohort with the aid of a classification algorithm. The classification algorithm generates a class label for the patient sample: either VeriStrat “good”, VeriStrat “poor”, or VeriStrat “indeterminate.” In multiple clinical validation studies it has been shown that patients, whose pre-treatment serum/plasma was VeriStrat “good”, have significantly better outcome when treated with epidermal growth factor receptor inhibitor drugs than those patients whose sample results in a VeriStrat “poor” signature. In few cases (less than 2%) no determination can be made, resulting in a VeriStrat “indeterminate” label. VeriStrat is commercially available from Biodesix, Inc., the assignee of the present invention, and is used in treatment selection for non-small cell lung cancer patients.
The VeriStrat test was developed from analysis of a multi-institutional study of NSCLC patients treated with gefitinib. The test was developed using a training set of pre-treatment serum samples from patients who experienced either long term stable disease or early progression on gefitinib therapy. Mass spectra (MS) from these patients' serum samples were used to define 12 mass spectrometry features (i.e. spectral peaks), differentiating these two outcome groups. The test utilized eight of these features based on a k-nearest neighbors (KNN) classification scheme and its parameters optimized using additional spectra from the training cohort. The test was further qualified in a blinded fashion on the pre-treatment serum of two independent cohorts of patients who were treated with gefitinib or erlotinib. These studies confirmed that patients classified as VeriStrat Good (VSG) had better outcome than patients classified as VeriStrat Poor (VSP) (Hazard Ratio [HR] of death=0.43 P=0.004 in one cohort, HR of death=0.33 P=0.0007 in the other). The test was shown to correlate with clinical outcome following epidermal EGFR TKI therapy, but not following chemotherapy or post-surgery as there was no statistically significant difference seen in the overall survival (OS) of patients classified as VSG or VSP prior to receiving second-line chemotherapy (HR=0.74, P=0.42 in one cohort and HR=0.81, P=0.54 in another). In a third control cohort of patients with resected early-stage NSCLC, the HR for OS was 0.90 (P=0.79).
The VeriStrat test was later formally, prospectively qualified in a study known as the PROSE study. See Randomized Proteomic Stratified Phase III Study of Second-Line Erlotinib Versus Chemotherapy in Patients with Inoperable Non-Small Cell Lung Cancer, ClinicalTrials.gov # NCT00989690, presentation presented to 2013 ASCO conference, June 2013. In brief, PROSE was a multi-center, randomized, Phase 3 study of 285 patients with advanced NSCLC who had progressed after first line chemotherapy treatment. Patients were randomized 1:1 to receive either standard dose erlotinib or chemotherapy (docetaxel or pemetrexed at the Investigator's discretion), stratified by Eastern Cooperative Oncology Group (ECOG)-Performance Status, smoking status, and blinded pre-treatment VeriStrat classification. PROSE results confirm that patients classified as VSP have better survival on chemotherapy versus erlotinib, and that patients classified as VSG have similar OS when treated with erlotinib or chemotherapy. The study reached its primary objective of showing significant interaction between treatment outcome and VeriStrat classification with an interaction p-value of 0.031.
While the PROSE results confirm that VeriStrat is a useful test for the de-selection of erlotinib (i.e., those patients testing VSP do not obtain benefit from erlotinib and obtain better survival on chemotherapy), further review of the data indicated that a test that would identify patients likely to have superior survival on erlotinib over chemotherapy would be of additional clinical value. This unmet clinical need led to the development of a new test, described in this document, which makes this identification.